Viz.ai, the leader in AI-powered disease detection and intelligent care coordination, and the University of Texas Medical Branch at Galveston (UTMB), an academic health institution of The University of Texas System, have announced a collaboration to incorporate Viz Subdural software into the Chronic Subdural Hematoma Treatment with Embolization Versus Surgery Study (CHESS).
A chronic subdural hematoma (cSDH) is an old clot of blood on the surface of the brain beneath its outer covering. These liquefied clots most often occur in patients aged 60 and older who have brain atrophy, a shrinking or wasting away of brain tissue due to age or disease.
As the population ages, cSDH is predicted to be the most common cranial neurosurgical condition by 2030. CHESS will collect safety and efficacy data in patients with a moderately symptomatic convexity cSDH.
“CHESS is designed to be the first US randomized controlled trial to provide evidence on the safety and efficacy of standalone middle meningeal artery embolization (MMAE) vs surgical drainage using particle embolization,” said Peter Kan, MD, MPH, FRCSC, FAANS, Professor and Chair, Department of Neurosurgery, University of Texas Medical Branch. “Viz.ai’s Subdural tool holds the promise of efficiently identifying prospective participants, thereby aiding in the timely completion of this important trial.”
The Viz Subdural algorithm was approved in 2022 as the first SDH-specific AI-powered detection and care coordination platform with the ability to identify suspected cases of acute and chronic subdural bleeds, and then quickly notify the care team to review the patient case and make a treatment decision if necessary.
In the CHESS trial, Viz.ai will notify investigators immediately when it detects a patient who has suspected SDH and meets the eligibility criteria for enrollment. Study investigators can measure chronic bleeds and coordinate subject enrollment easily and securely through Viz.ai’s dynamic, mobile, HIPAA-compliant platform.
“Patient enrollment is the most time-consuming and costly aspect of the clinical trial process,” said Prem Batchu-Green, Vice President of Clinical at Viz.ai. “By deploying Viz.ai for the CHESS study, we are not only reducing the manual burden of patient identification on research staff but also improving SDH detection and workflow as well as increasing SDH awareness, physician engagement, clinical specialist attendance, and case collaboration.”
